Monday, December 23, 2013

Concurrency vs. Parallelism

Over the past few years there seems to have been an increasing number of discussions of the difference between concurrency and parallelism.  These discussions didn't seem very convincing at first, but over time a useful distinction did begin to emerge.  So here is another attempt at trying to distinguish these two:
  • Concurrent programming constructs allow a programmer to simplify their program by using multiple (heavy-weight) threads to reflect the natural concurrency in the problem domain.  Because restructuring the program in this way can provide significant benefits in understandability, relatively heavy weight constructs are acceptable.  Performance is also not necessarily as critical, so long as it is predictable.
  • Parallel programming constructs allow a programmer to divide and conquer a problem, using multiple (light-weight) threads to work in parallel on independent parts of the problem.  Such restructuring does not necessarily simplify the program, and as such parallel programming constructs need to be relatively light weight, both syntactically and at run-time.  If the constructs introduce too much overhead, they defeat the purpose.
Given sufficiently flexible parallel programming constructs, it is not clear that you also need traditional concurrent programming constructs.  But it may be useful to provide some higher-level notion of process, which forgoes the single-threaded presumption of a thread, but brings some of the benefits from explicitly representing the natural concurrency of the problem domain within the programming language.

ParaSail is focused on providing parallel programming constructs, but concurrent objects are useful for more traditional concurrent programming approaches, particularly when coupled with explicit use of the "||" operator.  It is an interesting question whether some higher-level process-like construct might be useful.

Thursday, November 21, 2013

First release of Javallel, Parython; new release 5.1 of ParaSail and Sparkel

The ParaSail family of languages is growing, with two more additions now available for experimentation.  We have made a new release 5.1 which includes all four members of the family -- ParaSail itself, Sparkel based on the SPARK subset of Ada, Javallel based on Java, and Parython based on Python.  Binaries plus examples for these are all available in a single (large) download:

As before, if you are interested in sources, visit:

The biggest change in ParaSail was a rewrite of the region-based storage manager (actually, this same storage manager is used for all four languages), to dramatically reduce the contention between cores/processors related to storage management.  The old implementation was slower, and nevertheless still had a number of race conditions.  This one is faster and (knock on wood) free of (at least those ;-) race conditions.

As far as how Javallel relates to Java, here are some of the key differences:
  1. Classes require a "class interface" to declare their visible operations and fields
  2. There is no notion of a "this" parameter -- all parameters must be declared
  3. There is no notion of "static" -- a method is effectively static if it doesn't have any parameters whose type matches the enclosing class; no variables are static
  4. You only say "public" once, in the class, separating the private stuff (before the word "public") from the implementation of the visible methods.
  5. Semi-colons are optional at the end of a line
  6. Parentheses are optional around the condition of an "if"
  7. "for" statements use a different syntax; e.g:
    •  for I in 1..10 [forward | reverse | parallel] { ... }
  8. "{" and "}" are mandatory for all control structures
  9. You can give a name to the result of a method via:  
    • Vector createVec(...) as Result { ... Result = blah; ... } 
    and then use that name (Result) inside as a variable whose final value is returned
  10. You have to say "T+" rather than simply "T" if you want to accept actual parameter that are of any subclass of T (aka polymorphic).  "T" by itself only allows actuals of exactly class T.
  11. Object declarations must start with "var," "final," or "ref" corresponding to variable objects, final objects, or ref objects (short-lived renames).
  12. There are no special constructors; any method that returns a value of the enclosing is effectively a constructor;  objects may be created inside a method using a tuple-like syntax "(a => 2, b => 3)" whose type is determined from context
  13. X.Foo(Y) is equivalent to Foo(X, Y)
  14. Top-level methods are permitted, to simplify creating a "main" method
  15. uses "and then" and "or else" instead of "&&" and "||"; uses "||" to introduce explicit parallelism.
  16. "synchronized" applies to classes, not to methods or blocks
  17. enumeration literals start with "#"
There are examples in javallel_examples/*.jl?, which should give a better idea of what javallel is really like.  Parython examples are in parython_examples/*.pr?

Thursday, November 14, 2013

Using ParaSail as a Modeling Language for Distributed Systems

The ACM HILT 2013 conference just completed in Pittsburgh, and we had some great tutorials, keynotes, and sessions on model-based engineering, as well as on formal methods applied to both modeling and programming languages.  One of the biggest challenges identified was integrating complex systems with components defined in various modeling or domain-specific languages, along with an overall architecture, which might be specified in a language like AADL or SysML or might just be sketches on a whiteboard somewhere.  A big part of the challenge is that different languages have different underlying semantic models, with different type systems, different notions of time, different concurrency and synchronization models (if any),  etc.  The programming language designer in me wants to somehow bring these various threads (so to speak) together in a well-defined semantic framework, ideally founded on a common underlying language.

One way to start is by asking how can you "grow" a programming language into a modeling language (without killing it ;-)?  ParaSail has some nice features that might fit well at the modeling level, in that its pointer-free, implicitly parallel control and data semantics are already at a relatively high level, and don't depend on a single-address-space view, nor a central-processor-oriented view.  As an aside, Sebastian Burckhardt from Microsoft Research gave a nice talk on "cloud sessions" at the recent SPLASH/OOPSLA conference in Indianapolis (,, and we chatted afterward about what a perfect fit the ParaSail pointer-free type model was to the Cloud Sessions indefinitely-persistent data model. Modeling often abstracts away some of the details of the distribution and persistence of processing and data, so the friendliness of the ParaSail model to Cloud Sessions might also bode well for its friendliness to modeling of other kinds of long-lived distributed systems.

ParaSail's basic model is quite simple, involving parameterized modules, with separate definition of interface and implementation, types as instantiations of modules, objects as instances of types, and operations defined as part of defining modules, operating on objects.  Polymorphism is possible in that an object may be explicitly identified as having a polymorphic type (denoted by T+ rather than simply T) and then the object carries a run-time type identifier, and the object can hold a value of any type that implements the interface defined by the type T, including T itself (if T is not an abstract type), as well as types that provide in their interface all the same operations defined in T's interface.

So how does this model relate to a modeling language like Simulink or a Statemate?  Is a Simulink "block" a module, a type, an object, or an operation (or something entirely different)?  What about a box on a state-machine chart?  For Simulink, one straightforward answer is that a Simulink block is a ParaSail object.  The associated type of the block object defines a set of operations or parameter values that determine how it is displayed, how it is simulated, how it is code-generated, how it is imported/exported using some XML-like representation, etc.  A Simulink graph would be an object as well, being an instance of a directed graph type, with a polymorphic type, say "Simulink_Block+," being the type of the elements in the graph (e.g. DGraph).

Clearly it would be useful to define new block types using the Simulink-like modeling language itself, rather than having to "drop down" to the underlying programming language.  One could imagine a predefined block type "User_Defined_Block" used to represent such blocks, where the various display/simulation/code-generation/import/export operations would be defined in a sub-language that is itself graphical, but relies on some additional (built-in) block types specifically designed for defining such lower-level operations.  Performing code-generation on these graphs defining the various primitive operations of this new user-defined block type would ideally create code in the underlying programming language (e.g. ParaSail) that mimics closely what a (ParaSail) programmer might have written to define a new block type directly in the (ParaSail) programming language.  This begins to become somewhat of a "meta" programming language, which always makes my head spin a little...

A practical issue at the programming language level when you go this direction is that, what was a simple interface/implementation module model, may want to support "sub-modules" in various dimensions.  In particular, there may be sets of operations associated with a given type devoted to relatively distinct problems, such as display vs. code generation, and it might be useful to allow both the interface, and certainly the implementation of a block-type-defining module to be broken up into sub-modules.  The ParaSail design includes this notion, which we have called "interface extensions" (which is a bit ambiguous, so the term "interface add-on" might be clearer).  These were described in:
but have not as of yet been implemented.  Clearly interface add-ons, for say, [#display] or [#code_gen], could help separate out the parts of the definition of a given block type.

A second dimension for creating sub-modules would be alternative implementations of the same interface, with automatic selection of the particular implementation based on properties of the parameters specified when the module is instantiated.  In particular, each implementation might have its own instantiation "preconditions" which indicate what must be true about the actual module parameters provided before a given implementation is chosen.  In addition, there needs to be some sort of a preference rule to use when more than one implementations' preconditions are satisfied by a given instantiation.  For example, presume we have one implementation of an Integer interface that handles 32-bit ranges of integers, a second that handles 64-bit ranges, and one that handles infinite range.  Clearly the 32-bit implementation would have a precondition that the range required be within +/- 2^31, the 64-bit one would require the range be within +/- 2^63, and the infinite-range-handling implementation would have no precondition.  If we were to instantiate this Integer module with a 25-bit range, the preconditions of all three of the implementations would be satisfied, but there would presumably be a preference to use the 32-bit implementation over the other two.  The approach we have considered for this is to allow a numeric "preference" level to be specified when providing an implementation of a module along with the implementation "preconditions," with the default level being "0" and the default precondition being "#true." The compiler would choose the implementation with the maximum preference level with satisfied preconditions.  It would complain if there were a tie, requiring the user to specify explicitly which implementation of the module is to be chosen at the point of instantiation.

Wednesday, November 6, 2013

Tech talk and Tutorial at SPLASH 2013 on parallel programming

I gave an 80-minute "tech talk" and a 3-hour tutorial on parallel programming last week at SPLASH 2013 in Indianapolis.  The audiences were modest but enthusiastic. 

The tech talk was entitled:

  Living without Pointers: Bringing Value Semantics to Object-Oriented Parallel Programming

Here is the summary:

The heavy use of pointers in modern object-oriented programming languages interferes with the ability to adapt computations to the new distributed and/or multicore architectures. This tech talk will introduce an alternative pointer-free approach to object-oriented programming, which dramatically simplifies adapting computations to the new hardware architectures. This tech talk will illustrate the pointer-free approach by demonstrating the transformation of two popular object-oriented languages, one compiled, and one scripting, into pointer-free languages, gaining easier adaptability to distributed and/or multicore architectures, while retaining power and ease of use.

Here is a link to the presentation:

The tutorial was entitled:

   Multicore Programming using Divide-and-Conquer and Work Stealing

Here is the summary for the tutorial:

This tutorial will introduce attendees to the various paradigms for creating algorithms that take advantage of the growing number of multicore processors, while avoiding the overhead of excessive synchronization overhead. Many of these approaches build upon the basic divide-and-conquer approach, while others might be said to use a multiply-and-conquer paradigm. Attendees will also learn the theory and practice of "work stealing," a multicore scheduling approach which is being adopted in numerous multicore languages and frameworks, and how classic work-list algorithms can be restructured to take best advantage of the load balancing inherent in work stealing. Finally, the tutorial will investigate some of the tradeoffs associated with different multicore storage management approaches, including task-local garbage collection and region-based storage management.
Here is a link to the presentation:

Comments welcome!


Monday, September 16, 2013

Parallelizing Python and Java

Designing and implementing ParaSail has been a fascinating process.  Achieving parallelism and safety at the same time by eliminating rather than adding features has worked out better than we originally expected.  One of the lessons of the process has been that just a small number of key ideas are sufficient to achieve safe and easy parallelism.  Probably the biggest is the elimination of pointers, with the substitution of expandable objects.  The other big one is the elimination of direct access to global variables, instead requiring that a variable be passed as a (var or in out) parameter if a function is going to update it.  Other important ones include the elimination of parameter aliasing, and the replacement of exception handling with a combination of more pre-condition checking at compile time and a more task-friendly event handling mechanism at run time. 

So the question now is whether some of these same key ideas can be applied to existing languages, to produce something with much the same look and feel of the original, while moving toward a much more parallel-, multicore-, human-friendly semantics.

Over the past year we have been working on a language inspired by the verifiable SPARK subset of Ada, now tentatively dubbed Sparkel (for ELegant, parallEL, SPARK).  For those interested, this experimental language now has its own website:

Sparkel has essentially all of the power of ParaSail, but with a somewhat more SPARK-like/Ada-like look and feel.  We will be giving a talk on Sparkel at the upcoming HILT 2013 conference on High Integrity Language Technology in Pittsburgh in November:

HILT 2013 is open for registration, and it promises to be a great conference, with great talks, tutorials, and panels about model checking, model-based engineering, formal methods, SMT solvers, safe parallelism, etc. (disclaimer -- please note the name of the program chair).

At the upcoming OOPSLA/SPLASH conference, we are giving a talk about applying these same principles to Python and Java.  Super-secret code names for the results of this effort are Parython and Javallel.  The announcement of this talk is on the web site:

If you are coming to OOPSLA/SPLASH, please stop by to hear the results.  We will also be adding entries over the next couple of months about some of the lessons learned in working to create a safe parallel language when starting quite explicitly from an existing language.

Thursday, September 12, 2013

Revision 4.7 of ParaSail alpha release now available

We are pleased to release alpha revision 4.7 of the ParaSail compiler and virtual machine, available at the same URL:
This release includes a large number of bug fixes, plus the following enhancements:
More support for operations on polymorphic types, including binary operations, where it is an error if the two operands have different underlying types, unless the operator is "=?". "=?" is a special case, where rather than an error, it returns #unordered if two polymorphic operands have different underlying types.  This now allows us to define a type like Set and have it work as desired, that is, as a Set holding (almost) any type.

We have a preference now for non-generic operations when there would otherwise be ambiguity between a normal operation and a generic operation of the same name.  This is relevant for the "|" operator on Univ_String.  We have now added To_String and From_String on Univ_String (these are identity operations), which means Univ_String now implements the "Imageable<>" interface.  The new preference rules prevents this from creating ambiguity on | .

We know allow {> ... <} for annotations as a substitute for { ... }.  This will allow us to eventually use { ... } for Set/Map constructors in the PARython dialect.  The new {> ... <} syntax makes annotations a bit more obvious, which seems like a good thing.

Even when still using "then"/"is"/"loop"/"of" instead of ":", we have made "end blah" optional.  Presumably project-specific rules might require "end blah" if the construct is too many lines long (e.g. more than 20 lines).

Highlighting information for the ConTEXT source text editor is under share/tools/... in a file named ConTEXT_ParaSail.chl courtesy of ParaSail user Arie van Wingerden.  Similar information for the "geshi" highlighting system (used by WikiPedia) is in a file called geshi_parasail.php.

Case statements over polymorphic objects are now supported where the case choice has an associated identifier, such as in:

    var E : Expr+ := ...
    case E of
      [L : Leaf] => ...
      [U : Unary] => ...
      [B : Binary] => ...
      [..] =>
    end case;

Note that the specified types for the case choices must be non-polymorphic types at the moment.  In a later release we will support having choices with polymorphic types, such as:

    [SE : Simple_Expr+] => ...
where presumably Simple_Expr is an extension of Expr.

We have added function types, of the form:

   func(X : Integer; Y : Float) -> Integer

Basically the same syntax as a "func" declaration but without the func's identifier.  To declare a "func" that takes another "func" as a parameter, you would now use syntax like:

 func Apply
   (Op : func(X : Integer) -> Integer; S : Vector)
     -> Vector

rather than the former syntax:

 func Apply
   (func Op(X : Integer) -> Integer; S : Vector)
     -> Vector

The syntax for lambda expressions has been simplified, so you only specify the names of the inputs, with no mention of the types of the inputs or the outputs.  For example:

  lambda (Y) -> 2*Y
is a lambda expression that doubles its input. A lambda expr can be passed as a parameter so long as the type of the formal parameter is a compatible "func" type.  So for example, given the above definition of "Apply", you could write:

 Apply (lambda(Y)->2*Y, [1, 2, 3])

and expect to get "[2, 4, 6]" as the result (presuming "Apply" does the natural thing).
We now share the lock between a polymorphic object and the non-polymorphic object it contains; we also share the lock between object and its parent part, if any.  Handle "continue loop"s that exit blocks. Source code has been restructured to more easily handle new parser using same underlying language semantics, to support "Parython" and other language parallelization efforts.

When a "new" type is declared (type T is new Integer<...>) we allow additional operations to be declared immediately thereafter in the same scope, and have them be visible wherever the type is later used, just as though a new module had been defined as an extension of the old module, and the new operations had been declared inside that new module.

When an expression does not resolve, we now provide additional diagnostics to help explain why.

Wednesday, July 3, 2013

ParaSail web site now available

We now have a (non-blog) web site for ParaSail:

We will use this in the future as the official starting point for reaching resources for ParaSail, and in particular, getting to the latest documentation and downloads.  We will also be creating some examples, and ideally an online Read-Eval-Print-Loop (REPL) for trying your own examples.  If you have questions or comments about this new website, the Google Group for ParaSail is the best place to post those comments/questions:

Thursday, April 18, 2013

Systems Programming with Go, Rust, and ParaSail

Here is a paper that goes with the talk I am giving this coming Tuesday, April 23 at the DESIGN West, aka Embedded Systems Conference, in San Jose, on a comparison between Go, Rust, and ParaSail.

If you are in the Bay Area, come on down.  The talk is ESC-218, on Tuesday April 23 from 2:00-3:00PM in Salon 3:

NOTE: Some browsers are having trouble with this version of the paper.  It was cut and pasted from a Word document, which is always dicey.  A PDF version of this paper is available on the ParaSail Google Group:

Systems Programming in the Distributed, Multicore World with Go, Rust, and ParaSail 
S. Tucker Taft
24 Muzzey Street, 3rd Floor
Lexington, MA  02421

The distributed, multicore train is stopping for no programmer, and especially the systems programmer will need to be ready to hop on to the distributed parallel programming paradigm to keep their systems running as efficiently as possible on the latest hardware environments.  There are three new systems programming languages that have appeared in the last few years which are attempting to provide a safe, productive, and efficient parallel programming capability.  Go is a new language from Google, Rust is a new language from Mozilla Research, and ParaSail is a new language from AdaCore.  This talk will describe the challenges these languages are trying to address, and the various similar and differing choices that have been made to solve these challenges.
Keywords multicore, distributed, parallel programming, systems programming language, Go language, Rust language, ParaSail language

1. Introduction
The distributed, multicore train is stopping for no programmer, and especially the systems programmer will need to be ready to hop on to the distributed parallel programming paradigm to keep their systems running as efficiently as possible on the latest hardware environments.  There are three new systems programming languages that have appeared in the last few years which are attempting to provide a safe, productive, and efficient parallel programming capability.  Go is a new language from Google [1], Rust is a new language from Mozilla [2], and ParaSail is a new language from AdaCore [3][4].
The designers of Go, Rust, and ParaSail all are facing a common challenge -- how to help programmers address the new distributed and multicore architectures, without having the complexity of programming going past that which is manageable by the professional, yet still human, programmer.  All programming languages evolve, and as a rule, they tend to get more complex, not less so.  If every time a new hardware architecture becomes important, the programming language is enhanced to provide better support for that architecture, the language can become totally unwieldy, even for the best programmers.  When the architectures changes radically, as with the new massively distributed and/or multicore/manycore architectures, this may mean that the language no longer hangs together at all, and instead has become a federation of sublanguages, much like a house that has been added onto repeatedly with a different style for each addition.
Because of the complexity curse associated with language evolution, when there is a significant shift in the hardware landscape, there is a strong temptation to start over in programming language design.  After many years of a relatively stable programming language world, we now see a new burst of activity on the language design front, inspired in large part by the sense that our existing mainstream languages are either not going to be supportive enough, or that they are becoming simply too complex in trying to support both the old and new hardware paradigms through a series of language extensions.
2. Go from Google
Go, Rust, and ParaSail all emerged over the past few years, each with its own approach to managing complexity while supporting parallelism.  Go from Google is the brain child of Rob Pike and his colleagues at Google.  Rob was at Bell Labs in the early Unix and C days, and in many ways Go inherits the C tradition of simplicity and power. Unlike C, storage management has been taken over by the Go run-time through a general purpose garbage collection approach, but like C, care is still needed in other areas to ensure overall program safety. 
From the multicore perspective, Go uses goroutines for structuring large computations into a set of smaller potentially parallel computations.  Goroutines are easy to create – essentially any stand-alone call on a function or a method can be turned into a goroutine by simply prefixing it with the word “go.”  Once a goroutine is spawned, it executes independently of the rest of the code.  A goroutine is allowed to outlive the function that spawns it, thanks to the support of the garbage collector; local variables of the spawning function will live as long as necessary if they are visible to the spawned goroutine.
For goroutines to be useful, they need to communicate their results back to the spawning routine.  This is generally done using strongly-typed channels in Go.  A channel can be passed as a parameter as part of spawning a goroutine, and then as the goroutine performs its computation it can send one or more results into the channel.  Meanwhile, at some appropriate point after spawning the goroutine, the spawner can attempt to receive one or more values from the channel.  A channel can be unbuffered, providing a synchronous communication between sender and receiver, or it can provide a buffer of a specified size, effectively creating a message queue. 

Communication between goroutines can also go directly through shared global variables.  However, some sort of synchronization through channels or explicit locks is required to ensure that the shared variables are updated and read in an appropriate sequence.

Here is an example Go program that counts the number of words in a string, presuming they are separated by one or more separator characters, dividing multi-character strings in half and passing them off to goroutines for recursive word counts:
 func Word_Count
   (s string; separators string) int = {
     const slen = len(s)
     switch slen {
       case 0: return 0 // Empty string
       case 1:          // single-char string
         if strings.ContainsRune
               (separators, S[0]) {
             return 0  // A single separator
         } else {
             return 1  // A single non-separator
       default:   // divide string and recurse
         const half_len = slen/2
         // Create two chans and two goroutines
         var left_sum = make(chan int)   
         var right_sum = make(chan int)
         go func(){left_sum <- span="" word_count="">
              (s[0..half_len], separators)}()
         go func() {right_sum <- span="" word_count="">
              (s[half_len..slen], separators)}()
         // Read partial sums
         // and adjust total if word was divided
         if strings.ContainsRune
              (separators, s[half_len-1]) ||
              (separators, s[half_len]) {
             // No adjustment needed
             return <-left_sum right_sum="" span="">
         } else {// Minus 1 because word divided
             return <-left_sum -="" 1="" right_sum="" span="">
2.1 Unique Features of Go
Go has some unusual features.  Whether a declaration is exported is determined by whether or not its name begins with an upper-case letter (as defined by Unicode); if the declaration is a package-level declaration or is the declaration of a field or a method, then if the name starts with an upper-case letter, the declaration is visible outside the current package.

Every Go source file begins with a specification of the package it is defining (possibly only in part).  One source file may import declarations from another by specifying the path to the file that contains the declarations, but within the importing code the exported declarations of the imported code are referenced using the imported file’s package name, which need not match that of the imported file’s filename.  Of course projects would typically establish standard naming conventions which would align source file names and package names somehow.

Go provides a reflection capability, which is used, for example, to convert an object of an arbitrary type into a human-readable representation.  The “%v” format in Go’s version of printf does this, allowing arbitrarily complex structs to be written out with something as simple as:

fmt.Printf(“%v”, my_complex_object)

Printf is implemented in Go itself, using the “reflect” package.

There are no uninitialized variables in Go.  If a variable is not explicitly initialized when declared, it is initialized by default to the zero of its type, where each type has an appropriately-defined zero, typically either the zero numeric value or the nil (aka “null”) pointer value, or some composite object with all components having their appropriate zero value.
2.2 What Go Leaves Out
Because complexity was a major concern during all three of these language designs, some of the most important design decisions were about what to leave out of the language.  Here we mention some of the features that Go does not have.

Go does not permit direct cyclic dependencies between packages.  However, the Go interface capability permits the construction of  recursive control or data structures that cross packages, because an interface declared in one package can be implemented by a type declared in another package without either package being directly dependent on the other.

Like C, Go has no generic template facility.  There are some builtin type constructors, such as array, map, and chan, which are effectively parameterized type constructors, but there is no way for the user to create their own such type constructor.  Unlike C, there is no macro facility which might be used to create something like a parameterized type.  Nevertheless, Go’s flexible interface and reflection capabilities allow the creation of complex data structures that depend only on the presence of a user-provided method such as Hash and the DeepEqual function of the reflect package.

Go does not allow user-defined operators.  Various operators are built in for the built-in types, such as int and float32.  Interestingly enough, Go does include built-in complex types (complex64 and complex128) with appropriate operators.

Go does not have exceptions.  However, functions and methods  may return multiple results, and often errors are represented by having a second return value called error that is non-nil on error.  Unlike in C, you cannot ignore such an extra parameter; unless you explicitly assign it to an object of name “_”.  When things go really wrong in Go, a run-time panic ensues, and presumably during development, you are tossed into a debugger.
3. Rust from Mozilla Research
The modern web browser represents one of the most complex and critical pieces of software of the internet era.  The browser is also a place where performance is critical, and there are many opportunities for using parallelism as a web page is “rendered.”  The Rust language arose originally as a personal project by one of the engineers at Mozilla Research (Graydon Hoare), and has grown now into a Mozilla-sponsored research effort.  Rust has been designed to help address the complexity of building components of a modern browser-centric software infrastructure, in the context of the new distributed multicore hardware environment.

Like Go, Rust has chosen to simplify storage management by building garbage collection into the language.  Unlike Go, Rust has chosen to restrict garbage collection to per-task heaps, and adopt a unique ownership policy for data that can be exchanged between tasks.  What this means is that data that can be shared between tasks is visible to only one of them at a time, and only through a single pointer at a time (hence an owning pointer).  This eliminates the possibility of data races between tasks, and eliminates the need for a garbage collector for this global data exchange heap.  When an owning pointer is discarded, the storage designated by the pointer may be immediately reclaimed – so no garbage accumulates in the global exchange heap.

Here is a Rust version of the Word Count program, recursing on multi-character strings with subtasks encapsulated as futures computing the subtotals of each string slice:

fn Word_Count
   (S : &str; Separators : &str) -> uint {
     let Len = S.len();
     match Len {
       0 => return 0; // Empty string
       1 => return    // one-char string
         if Separators.contains(S[0]) { 0 }
           else { 1 };  // 0 or 1 words
       _ =>           // Divide and recurse
         let Half_Len = Len/2;
         let Left_Sum = future::spawn {
           || Word_Count(S.slice
                 (0, Half_Len-1), Separators)};
         let Right_Sum = future::spawn {
           || Word_Count(S.slice
                 (Half_Len, Len-1), Separators)};
         // Adjust sum if a word is divided
         if Separators.contains(S[Half_Len]) ||
           Separators.contains(S[Half_Len+1]) {
             // No adjustment needed
              Left_Sum.get() + Right_Sum.get(); 
         } else {
             // Subtract one because word divided
              Left_Sum.get()+Right_Sum.get() – 1;

Rust does not have special syntax for spawning a “task” (Rust’s equivalent of a “goroutine”) nor declaring the equivalent of a “channel,” but instead relies on its generic template facility and a run-time library of  threading and synchronization capabilities.  In the above example, we illustrate the use of futures which are essentially a combination of a task and an unbuffered channel used to capture the result of a computation.  There are several other mechanisms for spawning and coordinating tasks, but they all depend on the basic tasking model, as mentioned above, where each task has its own garbage-collected heap for task-local computation (manipulated by what Rust calls managed pointers), plus access via owning pointers to data that can be shared between tasks (by sending an owning pointer in a message).
3.1 Rust Memory Management Performance
One of the major advantages of the Rust approach to memory management is that garbage collection is local to a single task.  By contrast, in Go each garbage collector thread has to operate on data that is potentially visible to all goroutines, requiring a garbage collection algorithm that synchronizes properly with all of the active goroutines, as well as with any other concurrent garbage collector threads (presuming garbage collection itself needs to take advantage of parallel processing to keep up with multithreaded garbage generation). 

In Rust, a conventional single-threaded garbage collector algorithm is adequate, because any given garbage collector is working on a single per-task heap.  Furthermore, storage visible via owning pointers needs no garbage collection at all, as releasing an owning pointer means that the associated storage can also be released immediately.
3.2 The Costs of Safety and Performance
One of the downsides of added safety and performance can be added complexity.  As we see, Rust has added safety by allowing access to sharable data only via pointers that give exclusive access to one task at a time, and added performance because garbage collection is single-threaded.  However, as a result, Rust needs several kinds of pointers.  In fact, there are four kinds of pointers in Rust, managed pointers (identified by ‘@’as a prefix on a type) for per-task garbage-collected storage, owning pointers (identified by ‘~’) for data that is sharable between tasks, borrowed pointers (identified by ‘&’) that can temporarily refer to either per-task or sharable data, and raw pointers (identified by ‘*’), analogous to typical C pointers, with no guarantees of safety.
4. ParaSail from AdaCore
The ParaSail language from AdaCore takes support for parallelism one step further than Go or Rust, by treating all expression evaluation in the language as implicitly parallel, while also embracing full type and data-race safety.  Rather than adding complexity to accomplish this, the explicit goal for ParaSail was to achieve safety and pervasive parallelism by simplifying the language, eliminating impediments to parallelism by eliminating many of the features that make safe, implicit parallelism harder.
4.1 What ParaSail Leaves Out
Some of the features left out of ParaSail include the following:

·       No pointers
·       No global variables
·       No run-time exception handling
·       No explicit threads, no explicit locking nor signaling
·       No explicit heap, no garbage collection needed
·       No parameter aliasing

So what is left?  ParaSail provides a familiar class-and-interface-based object-oriented programming model, with mutable objects and assignment statements.  But ParaSail also supports a highly functional style of programming, aided by the lack of global variables, where the only variable data visible to a function is via parameters explicitly specified as var parameters.   This means that the side-effects of a function are fully captured by its parameter profile, which together with the lack of parameter aliasing allows the compiler to verify easily whether two computations can safely be performed in parallel.

By design, every expression in ParaSail can be evaluated in parallel.  If two parts of the same expression might conflict, the expression is not a legal ParaSail expression.  The net effect is that the compiler can choose where and when to insert parallelism strictly based on what makes the most sense from a performance point of view.  Here, for example, is the Word Count example in ParaSail, where parallelism is implicit in the recursive calls on Word Count, without any explicit action by the programmer:

func Word_Count
  (S : Univ_String;
   Separators :
        Countable_Set := [' '])
   -> Univ_Integer is
    case |S| of
      [0] => return 0  // Empty string
      [1] =>
        if S[1] in Separators then
            return 0   // No words
            return 1   // One word
        end if
      [..] =>          // Divide and recurse
        const Half_Len := |S|/2
        const Sum := Word_Count
            (S[1 .. Half_Len], Separators) +
            (S[Half_Len <.. |S|], Separators)
        if S[Half_Len] in Separators or else
          S[Half_Len+1] in Separators then
            return Sum  // No adjustment needed
            return Sum-1   // Adjust sum
        end if
    end case
end func Word_Count

Although there is no explicit use of a parallel construct, the sum of the two recursive calls on Word_Count can be evaluated in parallel, with the compiler automatically creating a picothread for each recursive call, waiting for their completion, and then summing the results, without the programmer having to add explicit directives.
4.2 Implicit and Explicit Parallelism in ParaSail
Explicit parallelism may be specified if desired in ParaSail, or the programmer can simply rely on the compiler to insert it where it makes the most sense.  The general philosophy is that the semantics are parallel by default, and the programmer needs to work a bit harder if they want to force sequential semantics.  For example, statements in ParaSail can be separated as usual with “;” (which is implicit at the end of the line when appropriate), or by “||” if the programmer wants to request explicit parallelism, or by “then” if the programmer wants to disallow implicit parallelism.  By default the compiler will evaluate two statements in parallel if there are no data dependences between them. 

As another example of ParaSail’s implicit and explicit parallelism, the iterations of “for I in 1..10 loop” are by default executed in any order, including parallel if there are no data dependences between the loop iterations, while “for I in 1..10 forward loop” or “for I in 1..10 reverse loop” may be specified to prevent parallel evaluation, and “for I in 1..10 concurrent loop” may be used to specify that parallel evaluation is desired, and it is an error if there are any data dependences between the iterations.  In all these cases, the compiler will ensure that any parallel evaluation is safe and data-race free, and will complain if there are potential race conditions when parallel evaluation semantics are specified.
4.3 Simplicity breeds Simplicity in ParaSail
There is somewhat of a virtuous cycle that occurs when a programming language is simplified, in that one simplification can lead to another.  By eliminating pointers and a global heap from ParaSail, the language can provide fully automatic storage management without the need for a garbage collector.  Objects in ParaSail have value semantics meaning that assignment copies the value of the right-hand side into the left-hand side, with no sharing of data.  A built-in move operation is provided for moving the value of the right-hand side into the left-hand side, along with a swap for swapping values, thereby reducing the cost of copying while still preserving value semantics.

Every type in ParaSail has an extra value, called null, which is used to represent an empty or otherwise uninitialized value.  An object or component may have a null value of its type T only if it is declared to be “optional T”.  Optional components may be used to implement trees and linked lists, without the use of explicit pointers, and without the potential sharing issues associated with pointers, even if behind the scenes the compiler uses pointers to implement optional objects or components.  The availability of optional components effectively allows an object to grow and shrink, meaning that dynamic structures like hash tables, and higher level notions such as maps and sets, can be implemented in ParaSail without any explicit pointers, with the advantages of purely value semantics.

The lack of explicit pointers means that all objects in ParaSail effectively live on the stack, even though they may still grow and shrink.  Each scope has its own region, effectively a local heap that expands and contracts to hold the objects associated with the scope.  No garbage collector is needed because when an object goes out of scope, or an object or one of its component is set back to null, the associated storage may be immediately reclaimed, much like storage designated by owning pointers in Rust.  By simplifying the type model, the storage management in ParaSail is dramatically simpler and of higher performance, with no complex parallel garbage collection algorithm required.
5. Implementation Status and Conclusions
The programming language design world has been rejuvenated by the new challenges of distributed and multicore architectures.  Three new programming languages designed for building industrial-strength systems have emerged, Go, Rust, and ParaSail.  Each of these languages tries to make parallel programming simpler and safer, while still providing the level of power and performance needed for the most critical systems development tasks. 

From an implementation status point of view, Go is the most stable of these three new languages, with two compilers available, and a number of systems built with Go now being deployed.  Rust and ParaSail are still under development, but both are available for trial use, with Rust having early on achieved the compiler boot strap milestone, where the Rust compiler is itself implemented in Rust.  All three languages provide opportunities for the professional programmer to expand their horizons, and see what sort of new paradigms and idioms will become more important as we leave behind the era of simple sequential processing, and enter the era of massively parallel, widely distributed computing.
[1]    The Go Programming Language, Google Corporation,
[2]    The Rust Programming Language, Mozilla Research,
[3]    ParaSail: Less is More with Multicore, S. Tucker Taft, (retrieved 4/1/2013).
[4]    Designing ParaSail: A New Programming Language, S. Tucker Taft,